Using computational approaches to understand the dynamics and evolution of complex biological networks.
A wide variety of processes in the cell depend on the function of large complexes. A major focus of our lab involves using computational modeling to understand how macromolecular structures assemble from their component parts. Our work is aimed at providing fundamental insights into assembly mechanisms with the ultimate goal of developing strategies that could disrupt or enhance the assembly of medically relevant complexes. The results of our work also provide general rules that can be applied in the design and construction of synthetic complexes that will self-assembly efficiently.
In one set of projects, we are considering the assembly dynamics of macromolecular structures that contain rings. Many important complexes, such as the proteasome, the chaperonin GroEL, and the apoptosome (which is involved in programmed cell death) , consist either of single rings or of multiple rings stacked on top of one another. We have characterized some of the challenges that face simple ring-like structures as they assemble, and have discovered mechanisms that complexes could employ to assemble with optimal efficiency. We are currently extending this work to the study of stacked rings, with a particular focus on complexes for which there is experimental evidence of self-assembly. The predictions of these models are being tested experimentally through our collaborations with a number of groups at KU.
A second major focus of the lab involves understanding how cells process information from their environment. This task is often accomplished by signaling networks in which proteins interact with one another to propagate extracellular signals to the appropriate cellular response. The structure of these networks varies widely in evolution: bacteria, for instance, utilize highly linear signaling networks in which a single protein, called the sensor kinase, directly activates a second protein called the response regulator. In contrast, metazoan networks, like those found in human cells, are generally incredibly complex, with a massive degree of crosstalk between the receptors on the cell surface and the transcription factors that control cellular responses. Our lab is using a combination of computational modeling, data analysis, and collaborations with experimental groups to understand the evolutionary pressures that have led to such different global topologies.
Rowland, M. A. and Deeds, E. J. “Crosstalk and the Evolution of Specificity in Two-Component Signaling” Proc Natl Acad Sci USA 111, 5550, (2014). [PMID: 24706803]
Suderman, R. and Deeds, E. J. “Machines vs. Ensembles: Effective MAPK Signaling through Heterogeneous Sets of Protein Complexes” PLoS Comput Biol 9, e1003278, (2013). [PMID:24130475]
Rowland, M. A., Fontana, W. and Deeds, E. J. “Crosstalk and competition in signaling networks” Biophys J 103, 2389, (2012). Selected as a "Best of 2012" article in Biophysical Journal. [PMID:23283238]
Deeds, E. J., Krivine, J., Feret, J., Danos, V. and Fontana, W. “Combinatorial complexity and compositional drift in protein interaction networks” PLoS One 7, e32032, (2012). [PMID:22412851]
Deeds, E. J.*, Bachman, J. A. and Fontana, W.* “Optimizing ring assembly reveals the strength of weak interactions” Proc Natl Acad Sci USA 109, 2348, (2012). *co-corresponding authors [PMID:22308356]
Kolokotrones, T., Savage, V., Deeds, E. J. and Fontana, W. “Curvature in metabolic scaling” Nature 464, 753, (2010). [PMID:20360740]
Savage, V. M.*, Deeds, E. J.* and Fontana, W. “Sizing up allometric scaling theory” PLoS Comput Biol 4, e1000171, (2008). *co-first authors [PMID:18787686]
Deeds, E. J., Ashenberg, O., Gerardin, J. and Shakhnovich, E. I. “Robust protein-protein interactions in crowded cellular environments” Proc Natl Acad Sci USA 104, 14952, (2007). [PMID:17848524]
Deeds, E. J., Ashenberg, O. and Shakhnovich, E. I. “A simple physical model for scaling in protein-protein interaction networks” Proc Natl Acad Sci USA 103, 311, (2006). [PMID:16384916]
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